Prevalent Crimes in the United States Research Paper

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Updated: Jan 6th, 2024

Introduction

Crime is prevalent in the United States. Americans report incidents of crime on a daily basis. To help combat crime, it is imperative for law enforcement bodies to understand characteristics of a crime incident. For example, if there is one victim, what is the likelihood that there was one offender? Of the incidents reported, how many male and/or female victims are involved? What is the most prevalent crime? Additionally, it is crucial to understand locations that are susceptible to crime. Local, state, and federal law enforcers need this kind of information to assign resources effectively and to help seize offenders. In this regard, I will orient this research towards indentifying and analyzing such factors. Lastly, the paper will develop research examining the number of prisoners in the USA jails because of these crimes, recidivism, and the importance of education in tackling crime.

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Background

Crime in the United States has been on a decline since its peak in 1980s. However, the characteristics of offenses have taken a new turn whereby offenders use sophisticated methods such as extortion, kidnapping among others (Aucoin, 2011). Normally, these crimes are reported to law enforcement during or after the incident. A new school of thought suggests that the justice system perpetrates the problem instead of solving it. Consequently, over a third of minorities lack college education because when they are supposed to be obtaining an education they are sent to prison. Once they are outside, the time for them to attend college is gone. Hence, the justice system needs to be overhauled to ensure that it does not punish. Rather, it should find a way in which the perpetrator pays for their social misdeeds without denying them a life, which makes it worse because these are minority groups. Additionally, this does not frequently happen with white males because majorities are incarcerated at between 35 and 45. At this age, a person has completed college and started a family. Hence, there will be little sentiment when the person gets out of prison.

According to the government’s statistics (in the United States for example) approximately 1 million prisoners are released annually. Most of them offend again and return to prison together with the first time offenders. The United States government spends over $30 billion to construct prison facilities. If the government were to reduce this recidivism rates by half, it would save so much. Education provides individuals with confidence in life and an alternative from crime. Once educated, a prisoner can rely solely on oneself. A prisoner is able to raise a family when they have equal chances for the available opportunities (Kleck, 2004).

However, this is not possible in a case the opportunities are skewed. Once an individual raises their family well, chances of a generational circus of crime are grossly reduced. In the long term, this is beneficial to both the society and the economy. Education in prisons provides better ways of utilizing the free time that inmates have in prison. This free time may be used for planning other evil deeds and making life for other prisoners and superintendants hard. Provisions of education bring some order as prisoners are expected to be at particular centers at particular times (Borghans, 2005).

A study of close to 20 empirical studies suggests that higher education reduces the possibility for re-incarceration of prisoners from both genders. Without any education on average 80% of the prisoners who are released from prison, return there within five years. If they were to be educated, the rate of recidivism would reduce according to the level of education achieved. The higher the education level, the lower the chances of returning to prison. For prisoners who attain a bachelor’s degree around 6% are re-incarcerated, for those who attain an AA degree around 14% are re-incarcerated, for those who attain a Masters degree, there is a zero chance of re-incarceration. It is also crucial to note that while in prison, these convicts are always in a constant torment and a dangerous environment. This may be transferred to society.

Data and methods

In order to answer the question of characteristics of a crime incident, I will use GSS data from the 1978 to 2011. I will use seven variables. Two of the variables will be categorical variable. They are most serious incident offense (msioff) and Incident location (inc_loc). The other five are quantitative variables. They include count of victims in incident (vic_count), count of offenders in incident (off_cnt), count of victims under age 18 (vlt18), count of male victims in incident (vmale), and count of female victims in incident (vfemale). The respondents to these questions were the different police and incident reporting stations in the USA. Hence, it constitutes official data of actual events. I will use three variables to determine the relationships, for example, to assess the relationship between the count of offenders in incident (off_cnt) and count of victims in incident (vic_count). I will run the data through Regression Analysis to determine the relationships. Another relationship is Incident location (inc_loc) and count of offenders in incident (off_cnt) and/or count of victims in incident (vic_count). The relationship may be crucial in predicting the number of victims in a hostage crisis when the offenders are known. I will also conduct cross tabulations to establish relationships between different variables. Lastly, descriptive statistics will be crucial in determining different measures such as the mean and standard deviation (National Opinion Research Center, 2013).

Results

Descriptive statistics

Table 1 lists statistics for the various variables such as count of offenders in incident (off_cnt) and count of victims in incident (vic_count). Table 2 shows the frequency of occurrence of various serious offenses in percentages. As Table 2 shows, robbery is the most prevalent offense recorded 98% of the time. Figure 1 also shows these frequencies in a diagrammatic format (i.e. a bar graph). Table 3 shows how often an offense happens in various locations. Most crimes (28.6%) happen in highways while the least crimes happen in churches.

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Table 1: Descriptive statistics

Descriptive Statistics
NMinimumMaximumMeanStd. Deviation
StatisticStatisticStatisticStatisticStd. ErrorStatistic
Count of victims in incident223361.0019.001.3352.00494.73784
Count of offenders in incident223361.0015.001.4436.00568.84852
Count of victims under age 1819367.006.00.1556.00323.44973
Count of male victims in incident19367.0010.00.7723.00463.64406
Count of female victims in incident19367.008.00.4029.00428.59592
Valid N (listwise)19367

Table 2: Most serious offense frequency table

Most serious incident offense
FrequencyPercentValid PercentCumulative Percent
ValidMurder and Non-negligent Manslaughter60.3.3.3
Forcible Rape99.4.4.7
Robbery2217699.399.3100.0
Rape of a Male1.0.0100.0
Total22336100.0100.0
Most serious incident offense frequencies
Figure 1: Most serious incident offense frequencies

Table 3: Frequency of incident per location

Incident location
FrequencyPercentValid PercentCumulative Percent
ValidTerminal39.2.2.2
Bank6552.92.93.1
Bar2801.31.34.4
Church22.1.14.5
Commercial/Office8113.63.68.1
Constr. Site12.1.18.1
Conv. Store20409.19.117.3
Dpt. Store4341.91.919.2
Drug Store191.9.920.1
Field/Woods2281.01.021.1
Government/Public62.3.321.4
Grocery7173.23.224.6
Highway638628.628.653.2
Hotel6152.82.855.9
Jail15.1.156.0
Waterway20.1.156.1
Liquor St.130.6.656.7
Parking223310.010.066.7
Stor Fac4.0.066.7
Residence348915.615.682.3
Restaurant10084.54.586.8
School178.8.887.6
Gas Station8183.73.791.3
Specialty St.5502.52.593.7
Other/Unk13996.36.3100.0
Total22336100.0100.0

Cross tabulations and chi-squared tests of independence

Tables 4, 6, 8, 10, and 12 indicate that there is a relationship between every two variables been cross tabulated. The inherent chi square tests indicate very little values (Incident location * Count of victims in incident Cross tabulation in Table 4 indicates that most incidents have one victim in different locations. In fact, incidents with more than five victims are limited in any location. Table 6 that cross tabulates Count of victims in incident and Count of offenders in incident indicates that most of the single victims encounter one offender. That is, most offenders target one victim in many occasions (12350). The small value of significance of P indicates a strong relationship between the variables.

Table 4: Incident location and count of victims in incident cross tabulation

Incident location * Count of victims in incident Cross tabulation
Count
Count of victims in incidentTotal
1.002.003.004.005.006.007.008.009.0010.0011.0013.0015.0019.00
Incident locationTerminal38100000000000039
Bank44516324104213020100655
Bar22341830201100001280
Church17410000000000022
Commercial/Office48525149175012001000811
Constr. Site8310000000000012
Conv. Store1078860762240000000002040
Dpt. Store2511383653010000000434
Drug Store139361330000000000191
Field/Woods19920630000000000228
Government/Public49831010000000062
Grocery45819447112311000000717
Highway56526098826101000000006386
Hotel3881783651250000000615
Jail13200000000000015
Waterway17120000000000020
Liquor St.69471021100000000130
Parking188928047961010000002233
Stor Fac211000000000004
Residence2718519144652113412200003489
Restaurant54931474302115121000101008
School15219501100000000178
Gas Station5612173351100000000818
Specialty St.29620335113101000000550
Other/Unk1123217341726000000001399
Total1681943267732458550141244111122336

Table 5: Chi-Square Tests for Table 4

Chi-Square Tests
ValuedfAsymp. Sig. (2-sided)
Pearson Chi-Square2784.714a312.000
Likelihood Ratio2423.551312.000
Linear-by-Linear Association15.2251.000
N of Valid Cases22336
a. 270 cells (77.1%) have expected count less than 5. The minimum expected count is.00.

Table 6: Count of victims in incident and count of offenders in incident cross tabulation

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Count of victims in incident * Count of offenders in incident Cross tabulation
Count of offenders in incidentTotal
1.002.003.004.005.006.007.008.009.0010.0012.0015.00
Count of Count victims in incident1.0012350294010803137438172221016819
2.00286595932810941175100014326
3.004342059126123020000773
4.00113593622103110000245
5.003427984110100085
6.002315821001000050
7.00112100000000014
8.0073110000000012
9.002200000000004
10.002200000000004
11.000100000000001
13.001000000000001
15.000100000000001
19.000000100000001
Total158424216155448114362247321122336

Table 7: Chi-Square Tests for Table 6

Chi-Square Tests
ValuedfAsymp. Sig. (2-sided)
Pearson Chi-Square797.350a143.000
Likelihood Ratio406.411143.000
Linear-by-Linear Association316.8531.000
N of Valid Cases22336
a. 139 cells (82.7%) have expected count less than 5. The minimum expected count is.00.

Table 8: Count of offenders in incident and count of victims under age 18 cross tabulation

Count of offenders in incident * Count of victims under age 18 Cross tabulation
Count
Count of victims under age 18Total
.001.002.003.004.005.006.00
Count of offenders in incident1.001181914021322557113391
2.00328644576134213827
3.00120720238161101465
4.0036173142210453
5.001032281200136
6.0043113110059
7.001631110022
8.0051100007
9.0030000003
10.0011000002
12.0010000001
15.0010000001
Total168462160273591611219367

Table 9: Chi-Square Tests for Table 8

Chi-Square Tests
ValuedfAsymp. Sig. (2-sided)
Pearson Chi-Square304.961a66.000
Likelihood Ratio169.32566.000
Linear-by-Linear Association155.6901.000
N of Valid Cases19367
a. 62 cells (73.8%) have expected count less than 5. The minimum expected count is.00.

Table 10: Count of victims in incident and count of male victims in incident Cross tabulation

Count of victims in incident * Count of male victims in incident Cross tabulation
Count
Count of male victims in incidentTotal
.001.002.003.004.005.006.007.008.009.0010.00
Count of victims in incident1.004162987300000000014035
2.0016521884617000000004153
3.001792921791150000000765
4.003563644932000000243
5.0061619171880000084
6.0019119974000050
7.000621221000014
8.001151021100012
9.00000002101004
10.00001012000004
11.00000100000001
15.00000000000101
19.00000000000011
Total60361214489819362237111119367

Table 11: Chi-Square Tests for Table 10

Chi-Square Tests
ValuedfAsymp. Sig. (2-sided)
Pearson Chi-Square60365.051a120.000
Likelihood Ratio5102.734120.000
Linear-by-Linear Association2814.3451.000
N of Valid Cases19367
a. 118 cells (82.5%) have expected count less than 5. The minimum expected count is.00.

Table 12: Count of victims in incident and count of female victims in incident cross tabulation

Count of victims in incident * Count of female victims in incident Cross tabulation
Count
Count of female victims in incidentTotal
.001.002.003.004.005.006.007.008.00
Count of victims in incident1.0098954140000000014035
2.00204819341710000004153
3.002962821632400000765
4.006574633560000243
5.00251817175200084
6.0081213106100050
7.0002222600014
8.0011211321012
9.000111100004
10.000000030104
11.000000000101
15.000000010001
19.000000000011
Total12338646443290211623119367

Table 13: Chi-Square Tests for Table 12

Chi-Square Tests
ValuedfAsymp. Sig. (2-sided)
Pearson Chi-Square46533.083a96.000
Likelihood Ratio3190.22696.000
Linear-by-Linear Association3693.2621.000
N of Valid Cases19367
a. 95 cells (81.2%) have expected count less than 5. The minimum expected count is.00.

Table 14: Count of offenders in incident and Count of female victims in incident cross tabulation

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Count of offenders in incident * Count of female victims in incident Cross tabulation
Count
Count of female victims in incidentTotal
.001.002.003.004.005.006.007.008.00
Count of offenders in incident1.008096498224047141002013391
2.002657101012719661103827
3.0010793323815100001465
4.0032810414600100453
5.00102237300001136
6.00487400000059
7.00183100000022
8.005110000007
9.002100000003
10.001100000002
12.001000000001
15.001000000001
Total12338646443290211623119367

Table 15: Chi-Square Tests for Table 14

Chi-Square Tests
ValuedfAsymp. Sig. (2-sided)
Pearson Chi-Square530.944a88.000
Likelihood Ratio386.67588.000
Linear-by-Linear Association63.5601.000
N of Valid Cases19367
a. 85 cells (78.7%) have expected count less than 5. The minimum expected count is.00.

Linear regression results

Tables 16 to 24 examine relationships between different variables with the aim of establishing whether one variable influences the other. However, as the model summaries indicate, the R Square and adjusted R Square values are too small as to indicate any relationships. For example, Table 16 indicates that the count of offenders and count of victims have a small relationship. Only 20% of the model is accounted for by count of offenders. The same case applies to the other model summaries. Hence, it is safe to conclude that the variables in this model do not influence each other as to causality.

Table 16: Regression of Count of female victims in incident and Count of offenders in incident

Model Summary
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
1.457a.209.209.68687
a. Predictors: (Constant), Count of female victims in incident, Count of offenders in incident

Table 17: ANOVA for Table 16

ANOVAa
ModelSum of SquaresdfMean SquareFSig.
1Regression2416.81321208.4062561.345.000b
Residual9135.66219364.472
Total11552.47519366
a. Dependent Variable: Count of victims in incident
b. Predictors: (Constant), Count of female victims in incident, Count of offenders in incident

Table 18: coefficients for table 16

Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
BStd. ErrorBeta
1(Constant).966.01092.942.000
Count of offenders in incident.120.006.13621.281.000
Count of female victims in incident.576.008.44569.443.000
a. Dependent Variable: Count of victims in incident

Table 19: Model summary Count of offenders in incident

Model Summary
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
1.119a.014.014.73260
a. Predictors: (Constant), Count of offenders in incident

Table 20: ANOVA for Table 19

ANOVAa
ModelSum of SquaresdfMean SquareFSig.
1Regression172.4981172.498321.399.000b
Residual11986.86922334.537
Total12159.36722335
a. Dependent Variable: Count of victims in incident
b. Predictors: (Constant), Count of offenders in incident

Table 21: Coefficients for table 19

Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
BStd. ErrorBeta
1(Constant)1.186.010122.564.000
Count of offenders in incident.104.006.11917.928.000
a. Dependent Variable: Count of victims in incident

Table 22: Model summary

Model Summary
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
1.042a.002.002.84778
a. Predictors: (Constant), Most serious incident offense

Table 23: ANOVA for Table 22

ANOVAa
ModelSum of SquaresdfMean SquareFSig.
1Regression29.030129.03040.391.000b
Residual16052.00422334.719
Total16081.03522335
a. Dependent Variable: Count of offenders in incident
b. Predictors: (Constant), Most serious incident offense

Table 24: coefficients for Table 22

Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
BStd. ErrorBeta
1(Constant)3.929.39110.046.000
Most serious incident offense-.021.003-.042-6.355.000
a. Dependent Variable: Count of offenders in incident

Discussion

Returning to the original research question, it follows that cross tabulation and chi-squared tests are the most relevant analytical tools for this data. Additionally, descriptive statistics shows that majority of the offenses occur because of robbery. Hence, federal and state governments should dedicate their resources towards combating robbery. A continued incarceration of social and criminal offenders has not deterred crime. With the world population increasing by day, more and more people are finding themselves in prisons. Most of them are first time offenders and a considerably large number of recidivists. Because of this trend, it is logical to employ the use of education, especially higher education, as the social and fiscal alternative in tackling this menace.

There are far reaching positive effects of education on youths and ex prisoners. These effects have wide social and fiscal benefits. Education in itself is an avenue to employment after prison. With private and public partnership, this can bear fruits in the overall societal life. It reduces chances of an offender going back to crime and makes offenders responsible for their families. Further, education increases self-esteem, self-confidence, enables youths and ex prisoners to become role models, and most critically, increases their options in the larger society (Biraimah, 2005).

Mnemonics list

GSS VariableVariable Name
inc_locIncident location
vic_cntCount of victims in incident
off_cntCount of offenders in incident
vlt18Count of victims under age 18
msioffMost serious incident offense
vmaleCount of male victims in incident
vfemaleCount of female victims in incident

Reference List

Aucoin, Robert. “Information and Communication Technologies in International Education: A USA Policy Analysis.” International Journal of Education Policy and Leadership 6, no. 4 (2011): 1-11.

Biraimah, Karen. “Achieving Equitable Outcomes or Reinforcing Societal Inequalities? A Critical Analysis of UNESCO Education for All and the United States No Child Left Behind Programs.” Educational Practice and Theory 27, no. 2 (2005): 25-34.

Borghans, Heijke. “The Production and Use of Human Capital: Introduction.” Education Economics 13, no. 2 (2005): 130-133.

Kleck, Gary. “Measures of Gun Ownership Levels of Macro-Level Crime and Violence Research.” Journal of Research in Crime and Delinquency 41, no. 1 (2004): 3-36.

National Opinion Research Center, University of Chicago, General Social Surveys, 1972-2011: Cumulative Codebook. Chicago: National Opinion Research Center, 2013.

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